Effective and automatic association of information from multiple independent sources is a valuable tool for a wide range of applications including data mining, object and entity search and association, visual surveillance across a distributed source of visual inputs, and the analysis of intelligence gathering and inference from multiple independent sources of different types of information at different places and times. Such collections of multi-source data, visual and non-visual, is often huge and either widely disparate (random) or closely akin (ambiguous) in their apparent attributes, with underlying intrinsic associations often being highly context dependent, latent and sparse, as well as difficult to quantify for automatic discovery and data association, resulting in the proverbial search for a needle in a haystack. Furthermore, it is not always possible to determine what specific associations of attributes are important prior to search. While various techniques such as the use of prior models or human interaction can help to narrow the search space or guide search with the benefit of human experience, they offer limited benefits when used in isolation.
For visual search and object re-identification in applications such as visual surveillance and multi-source visual object search, effective and reliable automatic object attribute extraction and association is very challenging in a large pool of multi-source visual data collected from distributed cameras or other sensor capturing sources. For instance, matching or tracking people across disjoint and disconnected different camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, existing methods and apparatus aim to extract object entity attributes in general and visual features in particular that are both distinctive and stable under appearance changes. However, most object attributes such as visual features and their combinations from disjoint multi-sources are neither stable nor distinctive and thus cannot be used directly and indiscriminately for object entity association across different sources.
Therefore, there is required a method and system that overcomes these problems.